Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015

doi:10.1007/s11707-018-0713-0

Front. Earth Sci.

2019, Vol. 13

Issue (1) : 92-110 https://doi.org/10.1007/s11707-018-0713-0

RESEARCH ARTICLE

Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015

Yongfeng WANG¹, Zhaohui XUE²(

), Jun CHEN¹, Guangzhou CHEN¹

¹. School of Environment and Engineering, Anhui Jianzhu University, Hefei 230022, China
². School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China

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Abstract

Phenology has become a good indicator for illustrating the long-term changes in the natural resources of the Yangtze River Delta. However, two issues can be observed from previous studies. On the one hand, existing time-series classification methods mainly using a single classifier, the discrimination power, can become deteriorated due to fluctuations characterizing the time series. On the other hand, previous work on the Yangtze River Delta was limited in the spatial domain (usually to 16 cities) and in the temporal domain (usually 2000–2010). To address these issues, this study attempts to analyze the spatio-temporal variation in phenology in the Yangtze River Delta (with 26 cities, enlarged by the state council in June 2016), facilitated by classifying the land cover types and extracting the phenological metrics based on Moderate Resolution Imaging Spectrometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series collected from 2001 to 2015. First, ensemble learning (EL)-based classifiers are used for land cover classification, where the training samples (a total of 201,597) derived from visual interpretation based on GlobelLand30 are further screened using vertex component analysis (VCA), resulting in 600 samples for training and the remainder for validating. Then, eleven phenological metrics are extracted by TIMESAT (a package name) based on the time series, where a seasonal-trend decomposition procedure based on loess (STL-decomposition) is used to remove spikes and a Savitzky-Golay filter is used for filtering. Finally, the spatio-temporal phenology variation is analyzed by considering the classification maps and the phenological metrics. The experimental results indicate that: 1) random forest (RF) obtains the most accurate classification map (with an overall accuracy higher than 96%); 2) different land cover types illustrate the various seasonalities; 3) the Yangtze River Delta has two obvious regions, i.e., the north and the south parts, resulting from different rainfall, temperature, and ecosystem conditions; 4) the phenology variation over time is not significant in the study area; 5) the correlation between gross spring greenness (GSG) and gross primary productivity (GPP) is very high, indicating the potential use of GSG for assessing the carbon flux.

Keywords Yangtze River Delta MODIS NDVI ensemble learning land cover classification spatio-temporal phenology

Corresponding Author(s): Zhaohui XUE

Just Accepted Date: 25 July 2018 Online First Date: 06 September 2018 Issue Date: 25 January 2019

Cite this article:

Yongfeng WANG,Zhaohui XUE,Jun CHEN, et al. Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015[J]. Front. Earth Sci., 2019, 13(1): 92-110.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0713-0
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I1/92

Fig.1 Yangtze River Delta covering 26 cities from Jiangsu, Zhejiang, and Anhui provinces, including Shanghai City.

Fig.2 Land cover reference maps for Yangtze River Delta. (a) GlobeLand30, (b) Regions of interest chosen from GlobeLand30.

Fig.3 Flowchart of the methodology used in this study.

Fig.4 A toy example of VCA for pure sample selection. The points with different colors represent specific land cover types, and the points in the circle are likely to be the endmembers.

Fig.5 Graphical illustration of an ensemble learning-based classification scheme. The notation is as follows: Bagging- ensemble learning based on bootstrap; RoF- Rotation Forest; RS- Random Subspace; and RF- Random Forest.

Fig.6 Graphical illustration of the phenological metrics (^{Xue et al., 2014a}).

Tab.1 Definitions, abbreviations, and properties of different phenological metrics

Fig.7 Overall accuracy (OA) as a function of number of labeled samples per class. The notation is as follows: kNN- k nearest neighbor; LORSAL- multinomial logistic regression via variable splitting and augmented Lagrangian; SRC- sparse representation-based classification method; Bagging- ensemble learning based on bootstrap; RoF- Rotation Forest; RS- Random Subspace; and RF- Random Forest.

Tab.2 A comparison of different classifiers in terms of class-specific accuracy, OA, AA, k (100 labeled samples per class)

Tab.3 Statistical test between-classifter in terms of Kappa z-score and McNemar z-score (100 labeled samples per class)

Fig.8 Classification maps obtained by (a) kNN, (b) SVM, (c) LORSAL, (d) SRC, (e) Bagging, (f) RoF, (g) RS, and (h) RF. The following notation is used: kNN- k nearest neighbor; SVM- support vector machine; LORSAL- multinomial logistic regression via variable splitting and augmented Lagrangian; SRC- sparse representation-based classification method; Bagging- ensemble learning based on bootstrap; RoF- Rotation Forest; RS- Random Subspace; and RF- Random Forest.

Tab.4 Typical land cover phenological metrics after averaging the results obtained from 2001 to 2015

Fig.9 Typical land cover seasonal analysis with Raw and Fitted series marked as SOS (start of season) and EOS (end of season). (a) Cultivated land, (b) Forest, (c) Grassland, (d) Wetland, (e) Water bodies, and (f) Artificial surfaces.

Fig.10 Spatial visualization of averaged phenological metrics obtained from 2001 to 2015. (a) SOS, (b) EOS, (c) LOS, (d) BOS, (e) TOMS, (f) POS, (g) AOS, (h) ROG, (i) ROS, (j) GSG, and (k) NSG. The following notation is used: SOS- start of season; EOS- end of season; LOS- length of season; BOS- base level of season; TOMS- timing of mid-season; POS- peak value of season; AOS- amplitude of season; ROG- rate of grow-up; ROS- rate of senescence; GSG- gross spring greenness; and NSG- net spring greenness.

Fig.11 The values of different phenological metrics (as a function of year) obtained for different land covers. (a) SOS, (b) EOS, (c) LOS, (d) BOS, (e) TOMS, (f) POS, (g) AOS, (h) ROG, (i) ROS, (j) GSG, and (k) NSG. The following notation is used: SOS- start of season; EOS- end of season; LOS- length of season; BOS- base level of season; TOMS- timing of mid-season; POS- peak value of season; AOS- amplitude of season; ROG- rate of grow-up; ROS- rate of senescence; GSG- gross spring greenness; and NSG- net spring greenness.

Fig.12 Correlation analysis of SOS and EOS with the yearly accumulated rainfall and the average temperature. (a) SOS vs. rainfall; (b) EOS vs. rainfall; (c) SOS vs. temperature; (d) EOS vs. temperature.

Fig.13 Correlation analysis of GPP versus GSG.

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